Factors Influencing the Use of Food Storage Structures by Agrarian Communities in Northern Uganda

18 Pages·2017·1.67 MB·English

by  Charles Owach

Journal of Agriculture, Food Systems, and Community Development
ISSN: 2152-0801 online
http://www.foodsystemsjournal.org
Factors influencing the use of food storage structures
by agrarian communities in Northern Uganda

Charles Owach a *
Food and Agriculture Organization of the United Nations

Godfrey Bahiigwa b
International Food Policy Research Institute

Gabriel Elepu c
Makerere University

Submitted April 21, 2016 / Revised July 6, August 28, October 12, November 18, and
November 23, 2016 / Accepted November 23, 2016 / Published online February 23, 2017
Citation: Owach, C., Bahiigwa, G., & Elepu, G. (2017). Factors influencing the
use of food storage structures by agrarian communities in northern Uganda.
Journal of Agriculture, Food Systems, and Community Development, 7(2), 127–144.
http://dx.doi.org/10.5304/jafscd.2017.072.003
Copyright © 2017 by New Leaf Associates, Inc.
Abstract passed a total sample of 782 households producing
Food storage at the national or global level is finger millet and/or beans (388 of which were
important due to its multifunctional roles of below and 394 above the poverty line). A binary
enhancing food access, nutrition, and income probit regression analysis was used to identify the
security at the national, community and household factors influencing the household use of improved
levels. This study assesses the importance of food storage structures.
storage structures and their utilization by farmers The findings indicated that only 22% of
cultivating finger millet (Eleusine coracana) and households used improved storage structures and
common beans (Phaseolus vulgaris) in Apac and Arua that usage depended on the age of the household
districts, Northern Uganda. The study encom-
Author Notes
a * Corresponding author: Charles Owach, Food and Agriculture This paper was developed from Charles Owach’s Ph.D. thesis
Organization of the United Nations; P.O. Box 521; Kampala, submitted in fulfillment of the requirements for the award of a
Uganda; [phone] or [phone]; doctorate degree at Makerere University, Kampala, Uganda.
[email protected]
This research is an assessment of the extent of use of
b Godfrey Bahiigwa, Office Head, International Food Policy improved food storage structures in two districts in Northern
Research Institute, Eastern and Southern Africa Regional
Uganda and associated factors that are associated with
Office; ILRI Campus, P.O. Box 5689; Addis Ababa, Ethiopia;
influencing storage use. The two districts covered (Apac and
[email protected]
Arua) represent the two farming systems in Northern Uganda,
c Gabriel Elepu, Department of Agribusiness & Natural that is, Northern and West Nile farming systems. The analysis
Resource Economics, Makerere University; P.O. Box 7062; was undertaken for households above and below the poverty
Kampala, Uganda; [email protected] line in order to guide policy decision-making.
Volume 7, Issue 2 / Winter 2016–2017 127
Journal of Agriculture, Food Systems, and Community Development
ISSN: 2152-0801 online
http://www.foodsystemsjournal.org

head, education level of the household head, While some SSA nations have made some
membership in a farmer group or association, improvements in achieving the Millennium
family size, and distance to market. The findings Development Goals1 by decreasing the share of
also indicate that the postharvest policies of the their populations suffering from extreme poverty
past did not have any significant effect on house- (hunger), 41% (25%) of the SSA population is still
hold access to improved storage technologies in reported to be extremely poor (undernourished)
the study areas. It is generally agreed that usage of (FAO, 2015; United Nations [UN], 2015). Since
improved storage structures leads such benefits as expenditures on food uses up much of household
postharvest losses reduction, product quality income in SSA and most residents depend on
conservation and increased duration of storage agriculture and allied activities as their source of
(World Food Programme [WFP], 2015). Thus we income, the impact on food security and poverty of
suggest that strategies to improve the usage of reducing food losses could be immense (Chauvin,
improved storage structures may be organizing Mulangu, & Porto, 2012). The objective of this
agrarian communities and reaching them with paper was to assess the use of food storage systems
carefully developed postharvest programs. This for households cultivating Eleusine coracana and
action could lead to higher usage rates of these Phaseolus vulgaris in Apac and Arua districts of
technologies in this region. Northern Uganda as a strategy for reducing food
loss.
Keywords Much of these losses in SSA take place at the
Food Losses; Improved Storage Structures; early stages of the food supply chain, and can be
Postharvest Losses; Agrarian; Households; attributed mainly to both pre- and postharvesting
Poverty; Smallholder; Northern Uganda losses (Kereth, Lyimo, Mbwana, Mongi, &
Ruhembe, 2013). On-farm storage of food crops
Introduction and Literature Review such as grains is commonly done by households
Globally, about one-third (or 1.3 billion tons) of using traditional storage structures (Thamaga-
total food products, valued at almost US$1 trillion, Chitja, Hendriks, Ortmann, & Green, 2004). In
is lost or wasted annually, almost one-half of which eastern and southern parts of Africa, a wide variety
is from developing countries (Food and Agricul- of storage structures are used, including wire cribs,
ture Organization of the United Nations [FAO], polythene bags, and metal silos (Kankolongo, Hell,
2011). In sub-Saharan Africa (SSA), food loss & Nawa, 2009) (see Figures 1–3).
accounts for 30% of total crop production and The postharvest losses of highly perishable
amounts to about US$4 billion a year (World Bank, food products such as fruits and vegetables, live-
2011). This value exceeded the value of total food stock and fishery products are even higher than for
aid received by1 SSA for the decade 1998–2008 and grains, and is attributable primarily to lack of cold
equals the value of all cereal imports to SSA in the chain (continuous cooling system for a commod-
period 2000–2007 (The World Bank, 2011). Post- ity), poor processing facilities, and marketing
harvest food losses contribute greatly to food, infrastructure (Hodges, Buzby, & Bennett, 2011).
nutrition, and income insecurity in this region. Reducing food losses by investing in improved and
user-friendly postharvest management offers

opportunities for enhancing food security and
1 According to the United Nations Millenium Project website,
household incomes in SSA without incurring any
“The Millennium Development Goals (MDGs) are the world’s
additional production costs (Hodges et al., 2011).
time-bound and quantified targets for addressing extreme
poverty in its many dimensions-income poverty, hunger, Improving postharvest management techniques
disease, lack of adequate shelter, and exclusion—while can also help build resilience against current and
promoting gender equality, education, and environmental future climate-related shocks and reduce the need
sustainability. They are also basic human rights—the rights of
for expanding farmland and damage to environ-
each person on the planet to health, education, shelter, and
mental services, including carbon sequestration
security” (United Nations, n.d., para. 2). The first goal is to
(Stathers, Lamboll, & Mvumi, 2013).
eradicate extreme hunger and poverty.
128 Volume 7, Issue 2 / Winter 2016–2017
Journal of Agriculture, Food Systems, and Community Development
ISSN: 2152-0801 online
http://www.foodsystemsjournal.org

Figure 1. Wire Crib Figure 2. Polythene Bags Figure 3. Metal Silo
In addition to building capacity of smallholder super grain bags, zero fly bags, plastic silos, metal
farmers in postharvest management, both hermetic silos, grain safes, and improved granaries (Figures
and nonhermetic storage technologies have been 4–8). Such structures were tested by farmers in
introduced and tested for efficacy in a number of Uganda and Burkina-Faso and found to be effec-
SSA countries (WFP, 2015). These storage tech- tive in reducing postharvest food losses by 98%,
nologies have proven to be effective in reducing regardless of the crop and duration of storage
food losses. Some of these technologies include (WFP, 2015).

Figure 4. Super Grain Bag Figure 5. Zero Fly Bag Figure 6. Plastic Silo (example of
a sealed grain container)

Figure 7. Grain Safe Figure 8. Improved Granary
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A number of projects related to food storage in Benin seems to have the lowest adoption rates,
innovations have attempted to link smallholder yet when storage technologies are developed using
farmers to markets and credit institutions as ways participatory approaches, adoption rates of 80%
to boost their incomes (Bouquet Wampfler, & were reported (Affognon et al., 2015). In Tanzania,
Ralison, 2009; Coulter & Onumah, 2002; a 55% to 64% uptake rate of extension knowledge
KENFAP, 2011; United States Agency for on control of larger grain borer by farmers was
International Development–Livelihoods and recorded (Golob, 1991). However, the rate of
Enterprises for Agricultural Development adherence to using such structures by the farmers
[USAID-LEAD], 2012; Woomer & Mukhwana, was only 17%. This could be attributed to various
2004). Such initiatives have used a participatory socio-economic factors, such as inadequate capital
approach, as in the case of maize in rural areas of for the high costs of the technologies, limited tech-
Kenya (Woomer & Mukhwana, 2004) and rice in nical know-how, and low price variability between
Madagascar and Tanzania (Coulter & Onumah, seasons, which reduces returns to storage
2002). Smallholders in several African countries, (Affognon et al., 2015; Golob, 1991).
such as Niger, Madagascar, Zambia, Malawi, Moreover, there is growing evidence indicating
Kenya, and Uganda, have also participated directly low levels of participation among smallholders in
in donor-funded inventory credit or “warrantage” collective storage (Ton, de Grip, Lançon, Onumah,
system for grains (Bouquet et al., 2009). Similar to & Proctor, 2014). In Uganda, only 23% of existing
the above strategies is the warehouse receipt sys- capacity of certified warehouses was utilized in
tem also introduced to many countries in eastern 2011, with a majority (90%) of the grain depositors
and southern Africa (Coulter & Onumah, 2002). being traders; some warehouses did not have a
Smallholders in SSA are in one way or the other single farmer registered to participate (USAID-
willing to adopt improved structures for posthar- LEAD, 2012). The low utilization of the store-
vest technologies at the household level. This houses was attributed mainly to their poor location,
indicates that there is considerable potential for lack of trust in the storage management arrange-
these smallholders to participate directly in collec- ments (including bad memories by farmers of the
tive storage at the community and national levels. functioning of some cooperatives in the past), and
The adoption rate for improved grain storage unfavorable terms of storage for the farm produce
technologies among smallholder farmers in SSA at (e.g., a lack of cash advances to farmers at the time
the household level, however, is variable (12.7%– of farm commodity deposit, even though farmers
74%), with most studies indicating adoption rates often need cash for domestic and social needs)
below 50% (Aguessy, 2009, as cited in Affognon, (USAID-LEAD, 2012). The same situation applies
Mutungi, Sanginga, & Borgemeister, 2015; Arouna, to Kenya, where 90% of warehouse grain deposits
Adegbola, & Biaou, 2011; Moussa, Lowenberg- were from large-scale farmers and traders, leaving
DeBoer, Fulton, & Boys, 2011). Some studies have only 10% of the space to smallholder farmer
reported abandonment rates as high as 56% to groups, a situation due mainly to high storage fees
73% (Adegbola, 2010; Affognon, Mutungi, and the relatively small volumes of commodities
Sanginga, & Borgemeister, 2015). Other studies offered by small farmers, even as farmer groups
reported adoption rates of 12.7% for metal drums (Jones & Lowenberg-DeBoer, 2014; KENFAP,
used for storage of cowpea in Benin (Moussa et al., 2011).
2011); 40.9% adoption of improved granaries for Various reasons have been attributed to poor
maize storage in Benin (Arouna et al., 2011); utilization of scientific postharvest management
adoption rates in Benin of 74%, 45%, and 41% for technologies by smallholder farmers in SSA. As
wooden granaries for storing maize cobs, and for indicated in a review by Affognon et al. (2015), the
metal cans and polyethylene bags for storage of problems can be associated with technological,
grains, respectively (Aguessy, 2009, as cited in socio-cultural, economic, and political reasons.
Affognon et al., 2015). With an adoption rate of Poor delivery of the scientific innovations, lack of
improved storage structures of only 5% , farmers knowledge of modern conservation methods or
130 Volume 7, Issue 2 / Winter 2016–2017
Journal of Agriculture, Food Systems, and Community Development
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techniques by many SSA smallholder farmers, and understanding of the types of storage structures
inappropriate postharvest technologies have been and factors influencing the use of improved storage
given as some of the reasons efforts to minimize structures are important as they can support
food losses have been hindered (Bediako, Chianu, evidence-based decision-making and policy
& Dadson, 2009; Obeng-Ofori, 2011). Results formulation, and contribute to attaining household
from a study in Tanzania reported that 96% of food security.
farmers have limited knowledge of methods of
proper postharvest management (Abass, Ndun- Materials and Methods
guru, Mamiro, Alenkhe, Mlingi, & Bekunda, 2014).
Smallholder farmers also may stick to traditional The Study Area
techniques of food conservation for fear of ven- The study covered two districts in Northern
turing into scientifically new but locally untested Uganda: Apac (longitude 32.15°–32.95°E and
methods available from research stations. Winnie- latitude 1.65°–2.25°N) and Arua (longitude 30.75°–
fridah and Manuku (2013) reported that the people 31.50°E and latitude 2.50°–3.35°N). Apac and
of Matabeleland in the southern province of Zim- Arua were selected as sample districts for a number
babwe preferred traditional ways of storage to of reasons. They are major food-producing districts
modern ways involving the use of agro chemicals and have long-standing peace and security. Apac
due to health concerns. The cost of constructing and Arua belong to the Northern and West Nile
improved storage structures has also hindered their farming systems, respectively. Apac and Arua
accessibility to farmers (Jones & Lowenberg- districts encompass key commercial towns (of the
DeBoer, 2014). Furthermore, stringent quality and same names) in Northern Uganda; this provides
quantity requirements associated with conservation good market conditions for price arbitrage. Apac
methods are a constraint for many farmers, as is and Arua districts’ human development index
the case with the warehouse storage system (HDI)2 of 0.508 and 0.551 in 2005 were 13% and
(Onumah, 2010). 5%, respectively below the national average of
In Uganda, postharvest interventions by pub- 0.581 (UNDP, 2007). The overall HDI for the
lic, private, and development partners, such as the Northern region in 2005 was 0.499, which was
Marketing and Agro-processing Strategy (MAPS) 14.1% lower than the national average of 0.581
formulated in 2004, have attempted in the past to during the same year (UNDP, 2007). Considering
promote the use of improved storage structures at some of the factors described above, Apac and
the household level by building the capacity of Arua districts were considered to be representative
farmers in postharvest loss management and of the Northern region. Furthermore, interregional
demonstrations on improved storage technologies comparisons indicate that the Northern region
(cribs, silos, etc.) (Republic of Uganda, Ministry of continues to lag behind in many socioeconomic
Agriculture, Animal Industry and Fisheries indicators (Republic of Uganda, Ministry of
[MAAIF], 2012a). However, postharvest food Finance, Planning and Economic Development
losses are still high, as exemplified by the grains [MFPED], 2014). The proportion of the
sector having 15–30% loss, attributable to the use population living in poverty in this region is still
of poor postharvest handling and storage technol- high at 43.7% (2012/13); while an improvement
ogies by farmers (Republic of Uganda, MAAIF, from 60.7% in 2005/06, it is high compared to the
2012a). A study by Ssewanyana and Kasirye (2010) overall national poverty levels of 19.7% and 31.1%
indicated that appropriate postharvest storage tech- in 2012/13 and 2005/06, respectively (MFPED,
nologies and conservation methods are important
in coping up with seasonal fluctuations in house-
hold dietary intakes. Yet Ssewanyana and Kasirye 2 The HDI is a holistic measure of overall human progress,
(2010) did not examine household usage of with special emphasis on living a decent life. It is an index
comprising three components that reflect the ends of the
improved storage structures or technologies for
development effort: life expectancy index, education index,
conserving agricultural and food products. Better
and GDP index, all weighted by one-third to obtain the HDI.
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2014). This region experiences recurrent food (US$29.40) per adult equivalent per month in
insecurity problems; expenditures on food were as 2008/09, considering the reference (base) period of
high as 56% of all rural household expenditures in 2005/06 (UBOS, 2013). From the total sample size
Northern Uganda in 2012/13 (MFPED, 2014). of 782 households, 394 households were above the
The choice of finger millet (Eleusine coracana) poverty line and 388 households were below the
and common bean (Phaseolus vulgaris) was informed poverty line. Data analyzed included storage types
by the fact that they are among the main staples of for food grains; age, sex, and education level of the
the people in Apac and Arua districts. Mukiibi household head; income status of the household
(2001) lists the main crops grown in Northern (above or below the poverty line); whether an
Uganda as being cassava (Manihot esculentus), maize extension worker had visited the household; the
(Zeamays), finger millet, sorghum (Sorghum bicolor), household’s access to credit; location variable
cowpeas (Vigna unguiculata), groundnuts (Arachis (district where the household was located); and
hypogaea), pigeon peas (Cajanus cajan), rice (Oryza household size, standardized in terms of how many
sativa), common beans (Phaseolus vulgaris), sweet adult-equivalents are in each family. Household adult
potatoes (Ipomea batatas), sunflower (Helianthus equivalencies were based on nutritional require-
annuus), soybeans (Glycine max), and cotton ments using consumption conversion factors as
(Gossypium species). In addition to contributing to recommended and contained in the World Health
food security, finger millet and beans are easily Organization’s (1985) nutrition guidelines.
storable, with lower post-harvest losses and lower Continuous variables (age of household head,
perishability than fruits, vegetables, meat, and fish number of years of formal education of household
(Hodges, Buzby, & Bennett, 2011). head, household size, and household income) were
measured at variable mean using t-statistics (Table
Data and Sampling Techniques 1). Categorical variables (sex of household head,
In this study, we analyzed data collected in the access of household head to extension and credit
Uganda census of agriculture 2008/09 by the services, and membership of household head in a
Uganda Bureau of Statistics (UBOS). UBOS used a farmer group or association) were measured as
stratified two-stage sample design to select small percentages using (χ 2) (Table 1).
and medium-scale households. The first stage Analysis of the storage structures used by
involved the selection of Enumeration Areas (EAs) households involved two steps. First, from the
with probability proportional to size (PPS) in the UBOS census 2008/09 dataset we identified and
study districts. The second stage involved the analyzed the various types of storage structures and
selection of households (ultimate sampling units) households using them using frequency distribu-
using systematic sampling, after stratification based tions. This was followed by categorizing the use of
on acres of cropland (UBOS, 2010a). The optimal various storage structures into two broad categories
number of 10 households (respondents) were (i.e., households using improved storage facility=1;
selected per EA (resulting in samples of 57 and 52 households using unimproved storage=0), after
in Apac and Arua districts, respectively) based on consulting with UBOS (the data source) and post-
cost ratio and intra-class correlation. After harvest specialists.3
exploratory data analysis, the pooled sample size
for the two study districts used in the analysis was Data Analysis
782 households. STATA12 statistical software was used for data
Households were categorized into two groups: analysis. Analysis of household use of improved
those above and those below the Northern region’s
poverty line. The national poverty line was 3 Key: Improved storage facility=1; unimproved storage
equivalent to UGX 62,545 (approximately facility=0. Improved granary=1; Unimproved granary=0;
In the house=0; Specific house/room=1; Under shelter
US$34.00; US$1=1,840 UGX at the time of this
outside=0; Cribs=1; Silos=1; Cold storage=1; Under-
study) per adult equivalent per month; the northern
ground=0; Over fireplace=0; Sealed containers=1;
region rural poverty line was UGX 54,174.47
Others=0.
132 Volume 7, Issue 2 / Winter 2016–2017
Journal of Agriculture, Food Systems, and Community Development
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Table 1. Characteristics of Households Growing Finger Millet and Bean in Apac and Arua Districts,
2008/09
All All
Households above Households below
households households
poverty line poverty line
above poverty below Overall
Characteristics Apac Arua Apac Arua line poverty line sample
Standard deviations in parentheses
43.2 46.6 43.7 46.3** 44.8 44.7 44.8
Age of household head (years)
(15.4) (20.8) (11.7) (13.3) (18.2) (12.4) (15.6)
Education of household head 6.2 6.0 5.9 5.7 6.2 5.8 6.0
(years in school) (2.8) (3.3) (3.9) (4.5) (3.0) (4.2) (3.6)
Household size (adult 3.3 3.0 5.4 5.8 3.1 5.6*** 4.3
equivalent) (2.0) (1.6) (1.9) (2.8) (1.9) (2.3) (2.3)
Household income (UGX per 69,527 70,956 39,726 37,293 70,185 38,710*** 52,253
capita per month) (12,771) (13,278) (11,785) (14,528) (13,004) (13,038) (20,312)
Sex of household head:
73.0 75.9 85.4 79.0 74.4 82.7*** 78.5
Male (%)
Household access to 45.6
29.0 6.4*** 9.3*** 18.3 30.4*** 24.3
extension services (%)
Household access to credit
5.3 2.7 7.1 15.4*** 4.1 10.6*** 7.3
(%)
Membership in farmer group
15.0 3.2*** 25.2 13.0*** 9.4 20.1*** 14.7
or association (%)
*** p < .01; ** p < .05
Note: The significance levels relate to comparisons across rows.
Source: Uganda Bureau of Statistics, 2010b.
storage structures was done using the probit model (if household adopts or uses improved storage sys-
developed following the utility theory as suggested tem); 0 if not using, and assuming an unobservable
by Greene (1997). The probit model makes it pos- threshold for Z = Z*, the household would adopt
i i
sible to generate the marginal effects of the explan- or use improved storage if Z exceeds Z*,.
i i

atory variables on the probability of adoption, and Assuming Zi*is also normally distributed like Zi,
the model has been used to establish factors affect- the parameters of the index Z can be estimated
i
ing adoption (Lapar & Pandey, 1999; Pindyek & and information about it obtained, and the values
Rubinfeld, 1991). The model presupposes that the of α and βi can be obtained. Information on Zi,
decision by the household to adopt or use or not to α, and β was obtained by taking the inverse of
i
adopt or use improved storage system would equation (1); hence,
depend on an unobservable index Z determined
i
by explanatory variables, where the bigger the (2) Z = A–1(Z ) = A–1 (P ) =α + βΧ + e
i i i i i
index, the greater the probability of the household
to use improved storage system. Where:
The expression takes the form: Z = Household decision to use or not to use
i
improved storage system (1 = household uses
(1) Z = α + βΧ improved storage system; 0 does not use); α =
i i i
Constant; Χ = Vector of explanatory vari-
i
Where: ables; β = Vector of parameters to be
i
Χ = Number of years of formal education of estimated; e = Error term
i
head of household, as an example;
α and β = parameters to be estimated. The specification of the probit analytical model
i
was as in equation (3) below.
Considering a dummy variable with values 1
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(3) Z = α + βΧ + βΧ…+ βΧ + e (MAAIF, 2012b). The main objective of subsis-
i 1 1 2 2 9 9
tence farmers is food security (consuming over
Where: 50% of their own production); semicommercial
Z = Dicotomous variable (1 = use of farmers sell at least 50% of their production, while
i
improved storage system; 0 = Otherwise) commercial farmers grow sugarcane, tea, rice, oil
α = Intercept; Χ= Age of household head palm, and coffee mainly on large-scale, specialized
1
(years); Χ= Sex of household head (Male=1; estates and sell the whole of their production after
2
Female=0); Χ= Education of household processing (MAAIF, 2012b).
3
head (years); Χ= Poverty line (Above=1; Results of the Uganda census of agriculture
4
Below=0); Χ= Extension visit (Yes=1; indicate that only 10% of the agricultural house-
5
No=0); Χ= Membership in farmer group or holds countrywide accessed credit during the
6
association (Yes=1; No=0); Χ= Obtained period 2002/03 to 2007/08 (UBOS, 2010b). The
7
credit (Yes=1; No=0); Χ= District (Apac=1; main source of credit was self-help groups (village
8
Arua=0); Χ = Household size (Adult savings loan associations), which provided loans to
9
equivalent); e = Error term 30.8% of households; microfinance institutions
(MFIs), which provided loans to 28.7% of house-
Results and Discussion holds; and families and friends, who provided loans
to 17.6% of households (UBOS, 2010b). Banks
Characteristics of Households provided loans to only 10.3% of agricultural house-
Almost three-quarters of households studied were holds (UBOS, 2010b). Self-help groups, MFIs, and
male-headed. Overall, there was no significant dif- families and friends provided the majority of loans
ference in age and education level of respondents to subsistence farmers and semicommercial farm-
below and above the poverty level (Table 1). ers; banks provided loans to commercial farmers
Irrespective of the type, household access to (UBOS, 2010b). With up to 76% of households
extension services, credit, and membership in required to provide collateral as security for loans,
farmer groups or associations were generally low in the need for collateral was the main constraint to
Apac and Arua districts (Table 1). Households accessing loans by agricultural households (UBOS,
below the poverty line were more likely (p < 0.01) 2010b). The main forms of collateral provided by
to access extension and credit services and to be in farmers were land titles (29.1%), character (23.1%),
organized farmer groups or associations than those crops (19.0%), and livestock (16.7%) (UBOS,
above the poverty line (Table 1). Households in 2010b). Organizing famers into groups and associa-
Apac district had better access to extension tions is being done by primarily by development
services and were more likely to be members of actors and government programs such as NAADS
farmer groups compared to those in Arua district, in order to enhance their access to agricultural
while those in Arua had better access (p < 0.01) to extension services and credit (MAAIF, 2012b).
credit services (Table 1). Better access by farmers
to extension services in Apac may be attributed to Household Production and Storage of Finger
the greater proportion of farmers being in groups, Millet and Beans
which facilitates extension outreach and adoption Results displayed in Table 2 indicate that respond-
of improved technologies (Table 1), in line with ents above the poverty line produced higher
results of a study on farmer field schools in Uganda quantities of finger millet and beans compared to
(Lwala, Elepu, & Hyuha, 2016). their counterparts below the poverty line, mainly
In the study districts, extension service delivery due to ownership of more agricultural land and
was provided mainly by National Agricultural better financial resources to hire farm labor. The
Advisory Services (NAADS). This agency cate- average land size was 2.2 ha (5.4 acres) and 1.18 ha
gorized farmers on the basis of their food security, (2.92 acres) for households above and below the
with 70% as subsistence farmers, 25% as poverty line, respectively (UBOS, 2010b). On
semicommercial, and 5% as commercial farmers average, households above the poverty line stored
134 Volume 7, Issue 2 / Winter 2016–2017
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Table 2. Per Capita Seasonal Finger Millet and Bean Production and Storage in Apac and Arua Districts,
2008/09 (in kg)
Households above Households below All All
poverty line poverty line households households
above below Overall
Crop Apac Arua Apac Arua poverty line poverty line sample
Finger Millet
Quantity produced 85.5 265.8*** 62.7 196.1** 214.9 106.1** 152.3
Quantity stored 30.2 78.7** 15.5 29.0** 55.2 20.4*** 39.2
Beans
Quantity produced 66.5 401.2*** 34.5 53.7 202.9 42.6*** 116.6
Quantity stored 18.4 62.9*** 8.5 18.1 35.5 12.0*** 22.9
*** p < .01; ** p < .05
Notes: The significance levels relate to comparisons across rows; 1 kg = 2.2 lbs.
Source: Uganda Bureau of Statistics, 2010b.
Table 3. Types of Storage Structures Used by Households in Apac and Arua Districts (%), 2008/09
Households above Households below All All
poverty line poverty line households households
above below Overall
Apac Arua Apac Arua
Storage type poverty line poverty line sample
Improved granary 7.8% 0% 4.4% 0% 3.7% 2.2% 2.9%
Unimproved granary 19.6 35.3 47.0 33.9 28.0 40.3 34.4
Inside house 70.3 64.6 60.5 81.5 67.2 71.2 69.4
Inside specific house or 15.7 11.5 18.3 25.0 13.5 21.7 17.8
room
Under shelter outside 0 6.2 4.3 11.7 3.3 8.1 5.8
Cribs 1.0 0 0 2.5 0.5 1.3 0.9
Over fireplace 2.9 20.2 6.1 21.5 12.1 14.0 13.1
Sealed containers 1.9 10.1 0 6.6 6.3 3.4 4.8
Others 0 1.7 0 4.1 0.9 2.1 1.5
Source: Uganda Bureau of Statistics, 2010b.
55.2 kg (121.7 lb.) of finger millet and 35.5 kg (78.3 proved granary (34.4%).4 The majority of house-
lb.) of beans per capita per season, while those holds below the poverty line (>71%) and above the
below the poverty line stored 20.4 kg (45.0 lb.) of poverty line (67%) stored the finger millet and
finger millet and 12.0 kg (26.5 lb.) of beans per beans in one of the rooms inside the house they
capita per season. Households in Arua district were living in: on the floor (Figure 9), in polythene
produced and stored larger quantities of finger bags (Figure 2), in pots (Figure 10), and in other
millet and beans compared to those in Apac district places. About 18% of households overall stored
(Table 2); this may be attributed to more intensive grain in a dedicated room of the house they were
farming and generally richer soils in Arua than in living in or a dedicated house; the rate varied
Apac (UBOS, 2010b). between households below (22%) and above (14%)
the poverty line (Table 3; Figure 11).
Types of Storage Structures Used A primary reason for the common practice of
As indicated in Table 3, the two most common
storage structures used by households were within
the residence of the farmers (69.4%) and in unim- 4 The sum is over 100% due to some households reporting
multiple storage types.
Volume 7, Issue 2 / Winter 2016–2017 135
Journal of Agriculture, Food Systems, and Community Development
ISSN: 2152-0801 online
http://www.foodsystemsjournal.org

Figure 9. Storing Grain on an Figure 10. A Clay Pot Used for Figure 11. Storing Grain in a
Open Floor Inside a House Storing Grain Inside a House Dedicated Room in a House
storing grain inside a house is to minimize risks leading to higher postharvest losses. A lower rate
such as postharvest losses from predators, pests, among households of improved granary usage for
and theft (UBOS, 2010b). Less than 3% of house- storage may be attributed to the higher cost of
holds used improved granary structures overall, building them, as has been found in previous
with households above the poverty level using studies (Adegbola, 2010, cited in Affognon et al.,
them at the slightly higher rate of 3.7% (Table 3). 2015).
An improved granary is a storage structure that is Furthermore, a few households stored their
substantially raised off the ground (by about 1 food products above the fireplace (4.8%) and in
meter or 3.3 feet) and supported by poles fitted sealed containers in their homes (13.1%) (Table 3).
with rodent guards, as shown in Figure 8, which One type of sealed container is a plastic silo (Figure
sits on concrete blocks and has walls made of mud 6). Most of the respondents who stored food in
and wattle and a top made of reeds covered with sealed containers were those who were above the
grass. An unimproved granary is a structure not poverty line. This suggests that they were fully
substantially raised off the ground (about 0.3 aware of the advantages of such containers in
meters or a foot off the ground), with the structure guarding against postharvest losses and were also
sitting on poles or low stones or poles not fitted able to afford them, as indicated in a report by the
with rodent guards, as shown in Figure 12, with WFP (2015).
walls made of mud and wattle and a top made of Despite government efforts to promote the
reeds and covered with grass. The contents of the use of cribs (an improved storage structure for
unimproved granary are prone to attack by rodents, grains, shown in Figure 1), less than 1% of

Figure 12. Unimproved Granary Figure 13. Storage Under Shelter Outside
136 Volume 7, Issue 2 / Winter 2016–2017